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[4/n] Migrate FP4/W4A8 CUTLASS kernels to torch stable ABI (#37503)

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Signed-off-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
This commit is contained in:
mikaylagawarecki
2026-03-31 13:21:13 -04:00
committed by GitHub
parent 0dd25a44ea
commit 7c080dd3c5
27 changed files with 1205 additions and 1016 deletions
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csrc/libtorch_stable/quantization/fp4/nvfp4_quant_kernels.cu Normal file
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/*
* Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <torch/csrc/stable/tensor.h>
#include <cuda_runtime_api.h>
#include <cuda_runtime.h>
#include <cuda_fp8.h>
#include "libtorch_stable/torch_utils.h"
#include "libtorch_stable/dispatch_utils.h"
#include "cuda_vec_utils.cuh"
#include "cuda_utils.h"
#include "launch_bounds_utils.h"
// Define before including nvfp4_utils.cuh so the header
// can use this macro during compilation.
#define NVFP4_ENABLE_ELTS16 1
#include "nvfp4_utils.cuh"
namespace vllm {
// Use UE4M3 by default.
template <class Type, bool UE8M0_SF = false>
__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, int32_t num_padded_cols,
Type const* __restrict__ in,
float const* __restrict__ SFScale,
uint32_t* __restrict__ out, uint32_t* __restrict__ SFout) {
using PackedVec = vllm::PackedVec<Type, CVT_FP4_PACK16>;
static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
(CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
"Vec size is not matched.");
// Precompute SF layout parameter (constant for entire kernel).
int32_t const numKTiles = (numCols + 63) / 64;
int sf_m = round_up<int>(numRows, 128);
int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
// Get the global scaling factor, which will be applied to the SF.
// Note SFScale is the same as next GEMM's alpha, which is
// (448.f / (Alpha_A / 6.f)).
float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
// Iterate over all rows and cols including padded ones -
// ensures we visit every single scale factor address to initialize it.
for (int rowIdx = blockIdx.x; rowIdx < sf_m; rowIdx += gridDim.x) {
if (colIdx < num_padded_cols) {
PackedVec in_vec;
int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
// If we are outside valid rows OR outside valid columns -> Use Zeros
bool valid = (rowIdx < numRows) && (elem_idx < numCols);
if constexpr (CVT_FP4_PACK16) {
ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec),
&reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
valid);
} else {
ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec),
&reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
valid);
}
auto sf_out =
cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
CVT_FP4_NUM_THREADS_PER_SF>(
rowIdx, colIdx, numKTiles, SFout);
auto out_val =
cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
in_vec, global_scale, sf_out);
// We do NOT write output for padding because the 'out' tensor is not
// padded.
if (valid) {
if constexpr (CVT_FP4_PACK16) {
int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
uint64_t packed64 =
(uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
} else {
out[inOffset] = out_val;
}
}
}
}
}
// Use UE4M3 by default.
template <class Type, bool UE8M0_SF = false>
__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
cvt_fp16_to_fp4_sf_major(int32_t numRows, int32_t numCols,
int32_t sf_n_unpadded, int32_t num_packed_cols,
Type const* __restrict__ in,
float const* __restrict__ SFScale,
uint32_t* __restrict__ out,
uint32_t* __restrict__ SFout) {
using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
(CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
"Vec size is not matched.");
int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
// Get the global scaling factor, which will be applied to the SF.
// Note SFScale is the same as next GEMM's alpha, which is
// (448.f / (Alpha_A / 6.f)).
float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
// Iterate over all rows and cols including padded ones -
// ensures we visit every single scale factor address to initialize it.
for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
if (colIdx < num_packed_cols) {
PackedVec in_vec;
int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
// If we are outside valid rows OR outside valid columns -> Use Zeros
bool valid = (rowIdx < numRows) && (elem_idx < numCols);
if constexpr (CVT_FP4_PACK16) {
ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec),
&reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
valid);
} else {
ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec),
&reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
valid);
}
auto sf_out =
sf_out_rowmajor_u8<uint32_t>(rowIdx, colIdx, sf_n_unpadded, SFout);
auto out_val =
cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
in_vec, global_scale, sf_out);
// We do NOT write output for padding because the 'out' tensor is not
// padded.
if (valid) {
if constexpr (CVT_FP4_PACK16) {
int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
uint64_t packed64 =
(uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
} else {
out[inOffset] = out_val;
}
}
}
}
}
} // namespace vllm
void scaled_fp4_quant_sm1xxa(torch::stable::Tensor const& output,
torch::stable::Tensor const& input,
torch::stable::Tensor const& output_sf,
torch::stable::Tensor const& input_sf,
bool is_sf_swizzled_layout) {
int32_t m = input.size(0);
int32_t n = input.size(1);
STD_TORCH_CHECK(n % 16 == 0, "The N dimension must be multiple of 16.");
STD_TORCH_CHECK(
input.scalar_type() == torch::headeronly::ScalarType::Half ||
input.scalar_type() == torch::headeronly::ScalarType::BFloat16,
"Unsupported input data type for quantize_to_fp4.");
int multiProcessorCount =
get_device_attribute(cudaDevAttrMultiProcessorCount, -1);
auto input_sf_ptr = static_cast<float const*>(input_sf.data_ptr());
auto sf_out = static_cast<int32_t*>(output_sf.data_ptr());
auto output_ptr = static_cast<int64_t*>(output.data_ptr());
const torch::stable::accelerator::DeviceGuard device_guard(
input.get_device_index());
auto stream = get_current_cuda_stream(input.get_device_index());
int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
// Grid, Block size. Each thread converts 8 values.
dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
int const numBlocksPerSM =
vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
if (is_sf_swizzled_layout) {
int sf_n_int = int(vllm::round_up(sf_n_unpadded, 4) / 4);
int32_t num_padded_cols =
sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD;
int grid_y = vllm::div_round_up(num_padded_cols, static_cast<int>(block.x));
int grid_x =
std::min(vllm::computeEffectiveRows(m),
std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
dim3 grid(grid_x, grid_y);
VLLM_STABLE_DISPATCH_HALF_TYPES(
input.scalar_type(), "nvfp4_quant_kernel", [&] {
using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
vllm::cvt_fp16_to_fp4<cuda_type, false><<<grid, block, 0, stream>>>(
m, n, num_padded_cols, input_ptr, input_sf_ptr,
reinterpret_cast<uint32_t*>(output_ptr),
reinterpret_cast<uint32_t*>(sf_out));
});
} else {
int num_packed_cols = n / CVT_FP4_ELTS_PER_THREAD;
int grid_y = vllm::div_round_up(num_packed_cols, static_cast<int>(block.x));
int grid_x = std::min(
m, std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
dim3 grid(grid_x, grid_y);
VLLM_STABLE_DISPATCH_HALF_TYPES(
input.scalar_type(), "nvfp4_quant_kernel", [&] {
using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
vllm::cvt_fp16_to_fp4_sf_major<cuda_type, false>
<<<grid, block, 0, stream>>>(
m, n, sf_n_unpadded, num_packed_cols, input_ptr, input_sf_ptr,
reinterpret_cast<uint32_t*>(output_ptr),
reinterpret_cast<uint32_t*>(sf_out));
});
}
}